Amandeep Yadav — Software Developer
Backend, Frontend, and platform tooling for distributed systems.
Open to new roles: Backend & platform engineering · Remote or Pune, India
Highlights
- current: Amdocs, India
- scale: 100M+ subscribers
- stack: Java + React + Kafka + Kubernetes
- subscribers served: 100M+ (carrier-scale charging)
- peak throughput: 1M+/s (rated events)
- uptime target: 99.99% (ordering + charging)
- records / day: 5M+ (billing aggregation)
Skills
Backend & testing: Java, Spring Boot, Python, Flask, JUnit, Mockito, Test NG, Postman.
Data & systems: Kafka, Redis, Cassandra DB, Couchbase, PostgreSQL, SQL & NoSQL DBs, DSA, OOP, Operating Systems, Computer Networks.
Cloud & delivery: Kubernetes, Docker, GitLab CI, Jenkins, Vault, AWS, Azure.
Frontend: React.js, Angular.js, JavaScript, TypeScript, Three.js.
Experience
Experienced Software Developer — Amdocs (07/2025 - Current)
- Production Deployment Tool: Developed internal dashboard tool using Java Spring Boot, Fabric8, GitLab REST API, React.js, and Vault to streamline feature testing. Enabled environment comparisons, changes, and caching for ~500 concurrent API calls—saving developers and testers 20% of testing/delivery time.
- AT&T Openet: Developed Openet CHF-CGF microservices using Java Spring Boot, Redis, Cassandra DB, Kubernetes, and Kafka to process accounting events from AT&T Network Access Servers. Enabled real-time charging for 100M+ subscribers' data/talk time usage—scaling to 1M+ events/sec with 99.99% accuracy.
- Metro By T-Mobile: Built microservices web apps using Java Spring Boot, Kafka, Angular.js, Git, Jenkins, and Kubernetes to deliver existing services to 20M+ Metro brand users post-acquisition. Orchestrated seamless, user-friendly access—boosting adoption by 15% and cutting service integration time by 40%.
Software Developer — Amdocs (07/2023 - 06/2025)
- TMO DGB: Built Digital Billing aggregation microservices for T-Mobile using Java Spring Boot, Kafka, Redis, Vault, Git, GitLab CI/CD, and Kubernetes. Bridged legacy SOA to new billing systems for 40M+ subscribers—aggregating 5M+ daily records with 99.9% reliability and 50% faster data sync.
- NorthStar: T-Mobile modernization project replaced legacy SOA with Java Spring Boot, Kafka, Camunda, Couchbase, PostgreSQL, Git, Jenkins, and Kubernetes microservices—adding high-volume ordering for 5M+ enterprise subscribers. Delivered 99.99% uptime, rolling updates, and async processing; contributed to 100+ features/fixes, cutting deployment time 30% and boosting order throughput 4x.
Backend Intern — Dubdub.ai (10/2021 - 01/2022)
- Multiprocessing: Optimized core platform service with Python multiprocessing in Flask/FFmpeg—strategically partitioning tasks across threads. Accelerated processing proportional to compute resources enabling approx. 300% higher throughput for 10K+ daily media tasks.
- Training Pipeline: Built data processing pipeline microservice using Python Flask, Google API client, and AWS S3/EC2, Docker for ML algorithm training. Automated ingestion from Google Sheets—processing 1000+ records daily, cutting prep time 70% and accelerating model training cycles by 50%.
- Gamify: Designed and launched inaugural monetization microservice using Python Flask on AWS Docker. Created ER/UML diagrams; enabled usage-based billing for early adopters—driving $200K initial revenue and 35% faster go-to-market.
Selected projects
- Production Deployment Tool (Internal Tooling) — Reduced developer and tester delivery friction by 20% through faster environment comparison and validation.
- AT&T Openet Microservices (Charging) — Processed charging/accounting events for 100M+ subscribers with 1M+ events/sec throughput targets.
- Metro By T-Mobile Platform (Telecom Enterprise) — Improved service adoption and integration speed during a post-acquisition platform migration.
- TMO Digital Billing Aggregation (Telecom Enterprise) — Aggregated 5M+ daily billing records for 40M+ subscribers while improving data sync speed by 50%.
- NorthStar Ordering Modernization (Performance Engineering) — Supported high-volume enterprise ordering, 99.99% uptime goals, and 4x order throughput improvement.
- Media Multiprocessing Service (Performance Engineering) — Increased throughput by approx. 300% for high-volume daily media processing workloads.
- ML Training Data Pipeline (Internal Tooling) — Automated ingestion for 1000+ daily records and cut model preparation time by 70%.
- Usage-Based Monetization Service (Charging) — Launched an early monetization service that helped drive $200K initial revenue and faster go-to-market.
Education
- Master of Computer Applications, NIT Trichy (07/2020 - 06/2023)
- Bachelor of Computer Applications, University of Rajasthan (07/2017 - 06/2020)
- Schooling, Rashtriya Military School (04/2010 - 03/2017)
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